To facilitate reproducibility, we release the source code for paper "Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation" on ECAI 2020.
Please contact me by email (j.pei@uva.nl) if any questions and cite our paper if you use any resources and/or codes in this repository.
Python 3 with pip
In repo directory:
- Using Conda:
cd multiwoz-mdrg
conda create --name multiwoz python=3.7 anaconda
source activate multiwoz
conda install --file requirements.txt
conda install pytorch torchvision -c pytorchTo download and pre-process the data run:
python multiwoz/Create_delex_data.py
To debug train.py, you can add the following parameteres to save time --debug=True --emb_size=5 --hid_size_dec=5 --hid_size_enc=5 --hid_size_pol=5 --max_epochs=2
To debug test.py, the parameters are: --debug=True --no_models=2 --beam_width=2
To train the model run:
python train.py [--args=value]
Some of these args include:
// hyperparamters for model learning
--max_epochs : numbers of epochs
--batch_size : numbers of turns per batch
--lr_rate : initial learning rate
--clip : size of clipping
--l2_norm : l2-regularization weight
--dropout : dropout rate
--optim : optimization method
// network structure
--emb_size : word vectors emedding size
--use_attn : whether to use attention
--hid_size_enc : size of RNN hidden cell
--hid_size_pol : size of policy hidden output
--hid_size_dec : size of RNN hidden cell
--cell_type : specify RNN type
To evaluate the run:
python test.py [--args=value]
// hyperparamters for model learning
--max_epochs : 20
--batch_size : 64
--lr_rate : 0.005
--clip : 5.0
--l2_norm : 0.00001
--dropout : 0.0
--optim : Adam
// network structure
--emb_size : 50
--use_attn : True
--hid_size_enc : 150
--hid_size_pol : 150
--hid_size_dec : 150
--cell_type : gru